1. Field of the Invention
The present invention relates generally to vehicle navigation systems, and more particularly, to systems and methods for estimating the bias and scale factor in a speed sensor and pitch sensor using a modified adaptive filter.
2. Background of the Invention
Many navigation systems rely on satellite-based global positioning (“GPS”) devices, including at least a number of which have been applied in automobile navigation systems. Conventional methods for correcting GPS positions are referred to as differential GPS (“DGPS”).
Integrated navigation systems (“INS”) are employed within vehicles in order to provide vehicle position and velocity information with respect to a specified reference frame. A typical INS determines estimates for the position and velocity of a vehicle based on a collection of data taken from inertial sensors such as acceleration and rate sensors mounted in the vehicle, as well as sensors based outside the vehicle, such as a GPS. Typically, the INS will use this sensed information, along with a model of vehicle motion behavior to form a set of navigation equations, in order to estimate vehicle position information and derivations therefrom. Conventional INS may be used in “turn-by-turn navigation” systems, “in vehicle dynamics” systems, and within proposed vehicle enhancements such as “adaptive cruise control.” A key element and/or function of the INS is the estimation of sensor errors used in the navigation equations. All sensors used by the INS have a scale factor that relates the sensor output to the sensed attribute, and a bias error; i.e., the sensor has a nonzero output even when the sensed attribute is zero. If the bias error or scale factor estimates are calculated incorrectly, the calculated vehicle position, heading, and/or speed will be in error, and the reliability of the INS will be undesirably reduced. This sensor error estimation is especially important in situations where data from the GPS may become unavailable such as under bridges or within tunnels.
Efforts have been made to reduce the impact of the scale factor and bias error estimation through the use of high quality inertial measurement equipment. However, the relatively high cost of such equipment is prohibitive for automotive applications. Hence, sensor error estimation is critical in conventional systems utilizing lower quality sensors. Conventional sensor error estimation is typically performed by developing sensor error models, and then implementing the model parameter estimation as augmented equations in the overall set of navigation equations, usually in a “Kalman” filter. The Kalman filter approach has desirable stability properties, but is somewhat limited, as it provides only statistical representations of errors, and requires the implementation to be added to the overall navigation equations. Other attempts at estimating these types of sensor errors have been made using neural networks. While neural networks have the advantage of learning in the presence of noise, they often require a relatively large number of learning examples, referred to as a training set, which are needed for the training phase. The required “training” process is relatively complicated, time consuming, and computationally intensive, and is therefore not suited to be carried out “on-line” or during the normal use of a vehicle.
What is needed are systems and methods for estimating bias and scale factor in speed and pitch sensors within an integrated navigation system.